©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Endosc. Jun 16, 2024; 16(6): 335-342
Published online Jun 16, 2024. doi: 10.4253/wjge.v16.i6.335
Published online Jun 16, 2024. doi: 10.4253/wjge.v16.i6.335
Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy
Kenneth W Chow, Matthew T Bell, Nicholas Cumpian, Maryanne Amour, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, United States
Ryan H Hsu, Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA 92093, United States
Viktor E Eysselein, Neetika Srivastava, Michael W Fleischman, Sofiya Reicher, Department of Gastroenterology, Harbor-UCLA Medical Center, Torrance, CA 90502, United States
Co-corresponding authors: Kenneth W Chow and Sofiya Reicher.
Author contributions: Study concept, study supervision, and design was performed by Reicher S; acquisition of data was performed by Chow KW, Bell MT, Cumpian N, and Amour M; analysis and interpretation of the data was performed by Chow KW, Hsu RH, Eysselein VE, Srivastava N, Fleischman MW, and Reicher S; statistical analysis was performed by Chow KW and Hsu RH; drafting of the manuscript was performed by Chow KW; all authors have read and approve the final manuscript.
Institutional review board statement: This study was approved by the Institutional Review Board (IRB number: 18CR-31902-01) of the Lundquist Institute at Harbor-UCLA.
Informed consent statement: Informed consent was waived by the Institutional Review Board.
Conflict-of-interest statement: Sofiya Reicher has served as a consultant for Boston Scientific. The rest of the authors have no conflicts of interest to disclose.
Data sharing statement: No additional data are available.
Corresponding author: Kenneth W Chow, MD, Doctor, Researcher, Department of Medicine, Harbor-UCLA Medical Center, 1000 W Carson Street, Torrance, CA 90502, United States. kwchow555@gmail.com
Received: February 28, 2024
Revised: April 16, 2024
Accepted: April 28, 2024
Published online: June 16, 2024
Processing time: 107 Days and 7.8 Hours
Revised: April 16, 2024
Accepted: April 28, 2024
Published online: June 16, 2024
Processing time: 107 Days and 7.8 Hours
Core Tip
Core Tip: This study analyzed the long-term impact of artificial intelligence (AI)-assisted colonoscopy in a diverse at-risk patient population undergoing diagnostic colonoscopy for positive colorectal cancer (CRC) screening tests or symptoms. It was found that in patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced screening metrics over high-quality colonoscopy. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.